import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
 
class NM(object):
    def __init__(self):
        self.df = pd.read_csv('timing/scenic_data.csv')

    def create_dataset(self, df, n_steps):
        """构造数据"""
        X, y = [], []
        for i in range(len(df) - n_steps):
            x_values = df[i:i+n_steps]
            x_values = pd.DataFrame(x_values)
            x_values.iloc[-1, x_values.columns.get_loc('count')] = 0
            X.append(x_values)
            y.append(df['count'][i+n_steps - 1])
        return np.array(X), np.array(y)

    def get_model(self):
        n_steps = 7  # 长度/天
        X, y = self.create_dataset(self.df, n_steps)
        
        # 训练集与测试集划分
        train_size = int(len(X) * 0.8)
        X_train, X_test = X[:train_size], X[train_size:]
        y_train, y_test = y[:train_size], y[train_size:]

        # 创建和训练LSTM模型
        model = tf.keras.models.Sequential()
        model.add(tf.keras.layers.LSTM(50, activation='relu', 
                                     return_sequences=True, 
                                     input_shape=(n_steps, X_train.shape[2])))
        model.add(tf.keras.layers.LSTM(50, activation='relu'))
        model.add(tf.keras.layers.Dense(1))
        model.compile(optimizer='adam', loss='mse')
        model.fit(X_train, y_train, epochs=50, validation_data=(X_test, y_test))

        # 评估
        loss = model.evaluate(X_test, y_test)
        print(f"测试损失: {loss}")

        # 保存模型
        tf.keras.models.save_model(model, 'timing/my_model.keras')
        
        return model

if __name__ == '__main__':
    nn = NM()
    nn.get_model()